Prompt Engineering Lab
Test prompts with real-time metrics. Learn patterns that improve AI responses.
Experiment with different prompt engineering techniques and see immediate feedback on performance.
System Prompt
User Prompt
Test your prompt to see response and metrics
Prompt Engineering Patterns
Few-Shot Learning
Provide examples to teach the AI the pattern you want. Works great for classification, formatting, and style matching.
Output: Positive
Input: "Terrible service"
Output: Negative
Input: "Your text here"
Output:
Chain-of-Thought
Ask the AI to think step-by-step. Improves reasoning, math problems, and complex analysis.
1. First, identify...
2. Then, calculate...
3. Finally, conclude...
Role-Based
Assign a specific role or persona. Shapes tone, expertise level, and response style.
with 10 years experience.
Explain this concept to
a beginner...
Constraint-Based
Set clear boundaries and requirements. Controls length, format, and content restrictions.
Use simple words only.
No technical jargon.
Target: 5th grade level.
Best Practices
✓ Do
- • Be specific and clear
- • Provide context and examples
- • Test multiple variations
- • Use delimiters for structure
- • Specify output format
✗ Avoid
- • Vague instructions
- • Assuming context
- • Overly complex prompts
- • Conflicting requirements
- • Ignoring token limits
💡 Understanding Metrics
Response Time
Total time from request to response. Affected by prompt length, model speed, and server load.
Token Count
Input + output tokens. Roughly 4 characters = 1 token. Affects cost and context limits.
Cost
Estimated cost based on token usage. Varies by model and provider pricing.
Provider
AI service used (Gemini, Groq, etc). Fallback system ensures availability.